About Michael – Chronic Diseases Data Science Project

Contact info

GUID: xx133
Email: xx133@georgetown.edu
Phone: 571-220-3128

Background

My academic journey has been defined by a profound fascination with time series analysis, machine learning, and natural language processing (NLP). During my previous professional experience, I honed my skills in handling large time series datasets, applying a variety of models to uncover underlying trends and patterns. This hands-on work provided a strong foundation in understanding and analyzing temporal data, fueling my passion for predictive modeling. Building on this foundation, I pursued advanced coursework and projects in machine learning, deep learning, and NLP, which significantly enhanced my technical expertise. I have developed proficiency in leveraging PyTorch for deep learning, enabling me to build and fine-tune neural network architectures for diverse applications. Additionally, I have applied cutting-edge NLP techniques to real-world datasets, gaining practical experience in text classification, sentiment analysis, and topic modeling. These academic and project-based experiences have reinforced my belief in the transformative potential of deep learning, particularly in domains such as time series forecasting, healthcare analytics, and text analysis.

Professional interest

My professional interests encompass the following areas:

Extensive experience in R and Python, with a focus on data analysis, modeling, and application development.
Expertise in models like ARIMA, SARIMA, and Bayesian Structural Time Series (BSTS), complemented by the integration of deep learning approaches such as LSTMs and Transformer-based architectures for temporal data.
Proficiency in building and training neural networks using PyTorch, with applications in NLP and time series analysis. Projects include implementing advanced NLP techniques such as text embeddings, transformer models, and sequence tagging pipelines.
Skilled in developing and deploying scalable applications, integrating machine learning models to create robust, data-driven solutions.

Education

Master of Science(MS) in Data Science and Analytics, Georgetown University(2025)
Bachelor of Science (BS) in Mathematics, University of Massachusetts Amherst (2021)

--- title: "About Michael" image: profile.jpg about: template: trestles image-width: 20em image-shape: round links: - icon: github text: Github href: https://github.com/Michaelx1010 format: html: page-layout: full code-fold: true code-copy: true code-tools: true code-overflow: wrap embed-resources: true --- ## Contact info - **GUID:** xx133 - **Email:** xx133\@georgetown.edu - **Phone:** 571-220-3128 ## Background My academic journey has been defined by a profound fascination with time series analysis, machine learning, and natural language processing (NLP). During my previous professional experience, I honed my skills in handling large time series datasets, applying a variety of models to uncover underlying trends and patterns. This hands-on work provided a strong foundation in understanding and analyzing temporal data, fueling my passion for predictive modeling. Building on this foundation, I pursued advanced coursework and projects in machine learning, deep learning, and NLP, which significantly enhanced my technical expertise. I have developed proficiency in leveraging PyTorch for deep learning, enabling me to build and fine-tune neural network architectures for diverse applications. Additionally, I have applied cutting-edge NLP techniques to real-world datasets, gaining practical experience in text classification, sentiment analysis, and topic modeling. These academic and project-based experiences have reinforced my belief in the transformative potential of deep learning, particularly in domains such as time series forecasting, healthcare analytics, and text analysis. ## Professional interest **My professional interests encompass the following areas:** - Extensive experience in R and Python, with a focus on data analysis, modeling, and application development. - Expertise in models like ARIMA, SARIMA, and Bayesian Structural Time Series (BSTS), complemented by the integration of deep learning approaches such as LSTMs and Transformer-based architectures for temporal data. - Proficiency in building and training neural networks using PyTorch, with applications in NLP and time series analysis. Projects include implementing advanced NLP techniques such as text embeddings, transformer models, and sequence tagging pipelines. - Skilled in developing and deploying scalable applications, integrating machine learning models to create robust, data-driven solutions. ### Education - Master of Science(MS) in Data Science and Analytics, Georgetown University(2025) - Bachelor of Science (BS) in Mathematics, University of Massachusetts Amherst (2021)